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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Muhammad Raditya Azanata
Universitas Telkom
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Dota 2 Hero Buff And Nerf Predictions Based On Professional Match Data Using Random Forest Muhammad Raditya Azanata; Muhamad Azrino Gustalika; Dimas Fanny Hebrasianto Permadi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2698

Abstract

Balancing updates (buffs and nerfs) are critical in Multiplayer Online Battle Arena games because small parameter changes can shift the competitive metagame and reduce hero diversity. This study proposed a data-driven pipeline to classify each Dota 2 hero as overpowered, underpowered, or balanced from professional match telemetry and to translate these classes into balance recommendations (nerf, buff, or balance). Most prior Dota 2 studies focus on match outcome or micro-event prediction and do not evaluate hero-centric balance recommendations against official patch actions across patch transitions. To address this gap, this work contributes a patch-to-patch external validation protocol that compares recommendations from patch t with developer actions in patch t+1 using patch notes. Professional match records were collected from public sources and aggregated per hero and per patch into combat, economy, and impact features (e.g., kills, deaths, assists, gold per minute, experience per minute, damage dealt, tower damage, and healing). Labels were derived from win-rate and pick-rate distributions using statistical control limits (μ ± kσ, k = 0.3) to ensure transparent and repeatable labeling. A Random Forest classifier was trained using grid-searched hyperparameters and evaluated using stratified 6-fold cross-validation with macro-averaged F1 to address class imbalance. Internal evaluation achieved 0.94 accuracy and 0.84 macro-F1. For external validation, recommendations from patch t were compared with official balance actions in patch t+1 across six consecutive transitions; accuracy ranged from 0.436 to 0.672 (mean 0.559), with the best result on 7.39b to 7.39c (84/125). These results indicated that professional telemetry could support interpretable balance monitoring and provide early signals for buff/nerf candidate review
Co-Authors Dimas Fanny Hebrasianto Permadi Muhamad Azrino Gustalika